{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "\n",
    "chess=pd.read_csv('./krkopt.data',header=None)\n",
    "x=chess.iloc[:,:6]\n",
    "y=chess.iloc[:,-1]\n",
    "\n",
    "# 将前6列数据中的字母“a、b、c、d、e、f、g、h”分别替换为数字“1、2、3、4、5、6、7、8”\n",
    "x.replace([chr(i) for i in range(97,123)],range(1,27),inplace=True)\n",
    "\n",
    "# 对前6列数据进行标准差标准化\n",
    "standard_scaler=preprocessing.StandardScaler()\n",
    "x=pd.DataFrame(standard_scaler.fit_transform(x))\n",
    "\n",
    "# 将最后一列数据中的“draw”替换为1，其他值替换为-1\n",
    "y=y.apply(lambda str:1 if str=='draw' else -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "training set: (3927, 6)\nvalidation set: (1684, 6)\ntesting set: (22445, 6)\n"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_trainval,x_test,y_trainval,y_test = train_test_split(x,y,test_size=0.8)\n",
    "x_train,x_val,y_train,y_val = train_test_split(x_trainval,y_trainval,train_size=0.7)\n",
    "\n",
    "print('training set:',x_train.shape)\n",
    "print('validation set:',x_val.shape)\n",
    "print('testing set:',x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "test_score: 0.9933615504566718\nbest_parameters: {'gamma': 0.1, 'C': 100}\n"
    }
   ],
   "source": [
    "\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "best_score=0.0\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        svm=SVC(gamma=gamma, C=C)\n",
    "        svm.fit(x_train,y_train)\n",
    "        score=svm.score(x_val,y_val)\n",
    "        if score>best_score:\n",
    "            best_score=score\n",
    "            best_parameters={'gamma':gamma,'C':C}\n",
    "            \n",
    "svm=SVC(**best_parameters)\n",
    "svm.fit(x_train,y_train)\n",
    "print('test_score:',svm.score(x_test,y_test))\n",
    "print('best_parameters:',best_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "precision_score:  0.9560487380330722\nrecall_score:  0.978619153674833\nf1_score:  0.9672022892361876\n"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score,recall_score,f1_score\n",
    "y_pred=svm.predict(x_test)\n",
    "print(\"precision_score: \",precision_score(y_test,y_pred))\n",
    "print(\"recall_score: \",recall_score(y_test,y_pred))\n",
    "print(\"f1_score: \",f1_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[-19.30575721 -21.71837389  -8.46253124 ...  -3.20085778 -24.57689722\n  -7.65179052]\nauc 0.9978110432424089\n"
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve,auc\n",
    "\n",
    "y_score=svm.decision_function(x_test)\n",
    "print(y_score)\n",
    "\n",
    "fpr,tpr,threshold=roc_curve(y_test,y_score)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(fpr,tpr)\n",
    "plt.xlabel(\"FP\")\n",
    "plt.ylabel(\"TP\")\n",
    "plt.title(\"ROC Curve\")\n",
    "\n",
    "print(\"auc\",auc(fpr,tpr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "impo"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}